Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC

Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. in this paper,a new feature extraction approach based on relative polypeptide composition is introduced. this approach could take into account the background distribution of a given k-mer under a markov model of order k-2,and avoid the curse of dimensionality with the increase of k by using a t-statistic feature selection strategy. the selected features are then fed to a support vector machine to perform the prediction. to verify the performance of our method,jackknife cross-validation tests are performed on four widely used benchmark datasets. comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction. © 2012 bentham science publishers.